<scp>IUF</scp>o<scp>ST</scp>'s strategy to strengthen food security in rural areas of developing countries
Bibliographic record
Abstract
Summary One sixth of the world's population is food insecure with many of these people living in Sub‐Saharan Africa. Food insecurity, hunger and malnutrition have multiple reasons, many of which are beyond the reach and capacity of the food science community to remediate. Knowledge of food science and technology can dramatically improve the situation wherever food insecurity exists. This knowledge can increase our understanding of the conditions under which agricultural produce has to be handled, processed and distributed after harvesting. To develop practical measures, the Food Security Task Force of the International Union of Food Science and Technology (IUFoST) is developing a strategy to expand and broaden the Food Science/Technology knowledge base in neglected geographical areas. Specifically, IUFoST is offering Food Science/Technology training material for non‐academic food industry entrepreneurs utilising distance education technology. Part of IUFoST's effort is the transfer of appropriate technologies for pilot‐scale processes which foster linkages between farmers and food industries and stimulate growing high value crops.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.109 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".